Data Engineering Manager

The Data Engineering Manager at ByHeart will oversee all aspects of the data engineering function, driving improvements in analytics tools and platforms. You will implement best practices to ensure data governance and performance, aligning our data strategies with business objectives. Your role includes building and maintaining our analytical environment, developing pipelines, dashboards, and responding to ad hoc data requests.

To be successful as a Data Engineering Manager at ByHeart, candidates should have 4-6 years of experience in data engineering, particularly in high-growth startup environments. Proficiency in SQL and Google Cloud is essential, alongside familiarity with data integration tools and business intelligence platforms. Strong communication skills and the ability to collaborate across functions are also crucial.

Key technical skills for a Data Engineering Manager at ByHeart include expert-level proficiency in SQL and BigQuery, advanced expertise with Google Cloud platform capabilities, and strong competence in dbt and GitHub for code management. Familiarity with third-party integration tools and business intelligence platforms like Looker is also highly desirable.

ByHeart promotes a family-first culture, offering flexible paid time off, generous parental leave policies, and wellness programs. The organization believes in creating a supportive environment where employees can thrive both personally and professionally while contributing to the mission of innovative infant nutrition.

As a Data Engineering Manager at ByHeart, you will have numerous opportunities for professional development. The role involves participating in project teams, mentoring others in analytics capabilities, and staying ahead of industry trends. ByHeart fosters a culture of learning and growth, preparing you for future leadership roles within the company.

When answering this question, outline specific projects where you used SQL to extract insights from datasets. Emphasize your understanding of database design and optimization techniques, as well as how you’ve leveraged SQL to deliver actionable results in past roles.

Explain your approach to data governance, including the practices you implement to maintain data quality and consistency. Discuss your experience with documentation, training staff on data standards, and monitoring compliance to ensure that data remains reliable and accurate.

Discuss your communication style and how you adapt it based on team dynamics. Share examples of successful collaborations, focusing on how you ensured alignment on project goals and navigated any challenges that arose during the course of collaboration.

Outline a specific challenge you faced while constructing a data pipeline. Describe the steps you took to analyze the issue, the strategies you employed to resolve it, and the final outcome, emphasizing your problem-solving abilities and technical expertise.

Share your experience in designing scalable analytics frameworks. Discuss your knowledge of cloud services and how you optimize costs while enhancing performance. Provide examples of past solutions designed for growth and efficiency.

Talk about your familiarity with various Google Cloud services like BigQuery, Dataflow, and other relevant tools. Describe specific projects in which you've utilized these technologies and how they contributed to achieving your data engineering goals.

Reflect on how you believe a Data Engineering Manager can nurture a data-driven philosophy within the company. Discuss strategies you would implement to encourage data literacy across teams and how you'd promote the use of analytics in decision-making.

Provide a specific instance where your data analysis influenced a significant business decision. Detail the data collected, the analytical methods employed, and the impact it had on the organization’s outcome or strategy.

Discuss your commitment to continuous learning in the rapidly-changing field of data. Mention resources you use, such as online courses, webinars, and industry publications. Share how you apply your learning to improve your work or influence your team's direction.

Emphasize the critical role documentation plays in maintaining clarity and continuity in data projects. Discuss how thorough documentation can enhance collaboration, ensure data integrity, and facilitate onboarding new team members into established workflows.